From the data view, we have seen a large-scale pre-training dataset named LUPerson being built by collecting unlabeled person images [1]}, which has demonstrated that CNN-based SSL pre-training on LUPerson dataset improves ReID performance compared with ImageNet-1k pre-traininng. However, following the same paradigm and replacing the backbone with Vision Transformers (ViTs) [2]} would get poor performance due to the huge differences between training CNNs and ViTs. It motivates us to explore an effective SSL pre-training paradigm for transformer-based person ReID in the first place. After thorough comparison between several Transformer-based self-supervised methods (MocoV3 [3]}, MoBY [4]} and DINO [5]}) with ViTs on the LUPerson dataset, it is found that DINO significantly outperforms other SSL methods and supervised pre-training on ImageNet, thus it is used as our following setup. Next, it is brought to our attention that, though with better performance, pre-training on LUPersonn needs a larger amount of computational resources due to the large amount of training data (3X of ImageNet-1K). Therefore, we propose to adopt conditional pre-training to speed-up the training process and further reduce the domain gap. As LUPerson is collected from web videos, a portion of the images are of low-quality or have great domain bias with the downstream ReID datasets, so conditional filtering shall be performed to downscale LUPerson (source domain) by selecting a relevant subset close to the downstream ReID datasets (target domain). However, previous works on conditional filtering  [6]}, [7]}, [8]}, [9]}, [10]} are mainly designed for close-set tasks by selecting data close to category labels or cluster centers of the target training data. Those methods will easily overfit the person IDs instead of the true target domain if directly applied to the open-set ReID task. We propose a metric named Catastrophic Forgetting Score (CFS) to evaluate the gap between pre-training and downstream data, inspired by the conclusions in catastrophic forgetting problems [11]}, [12]}, [13]}. For a pre-training image, the similarity between its features from the two proxy models (one is pre-trained on the source dataset and another is fine-tuned on the target dataset) can represent the similarity between the source and target domain. In this way, a subset of images with higher CFS scores can be selected from the pre-training data to perform efficient conditional pre-training. A preliminary theoretical analysis is conducted to justify the effectiveness of CFS.
